多面体集下多目标优化问题近似解的若干性质

研究了多目标优化问题的近似解. 首先证明了多面体集是 co-radiant集,并证明了一些性质. 随后研究了多面体集下多目标优化问题近似解的特殊性质.     

近似凸集的某些性质

主要研究了两类近似凸集的关系和性质.首先,举例说明两类近似凸集没有相互包含关系.其次,在近似凸集(nearly convex)条件下,证明了在一定条件下函数上图是近似凸集与凸集的等价关系.同时,考虑了近似凸函数与函数上图是近似凸集的等价刻画、近似凸函数与函数水平集是近似凸集的必要性,并用例子说明近似凸函数与函数水平集是近似凸集的充分性不成立.最后,基于近似凸函数和拟凸函数的概念,给出了近似拟凸函数的概念并研究了近似拟凸函数与水平集是近似凸集的等价刻画.     

集值优化问题在有效意义下的最优性条件

本文在赋范空间中,讨论集值优化问题的有效元导数型最优性条件.当目标映射和约束映射的下方向导数存在时,在近似锥次类凸假设下利用有效点的性质和凸集分离定理得到了集值优化问题有效元导数型Kuhn-Thcker必要条件,在可微Г-拟凸性的假设下得到了Kuhn-Tucker最优性充分条件;此外利用集值映射沿弱方向锥的导数的特性给出了有效解最优性的另一种刻画.     

向量优化中基于拟内部的弱C(\varepsilon)-有效解的标量化

首先获得了co-radiant集的一些拟内部性质. 进而在邻近C(\varepsilon)-次似凸性假设条件下, 建立了相应的择一性定理, 并给出了基于拟内部的集值向量优化问题弱C(\varepsilon)-有效解的线性标量化结果.     

集值优化问题强有效解的Kuhn Tucker最优性条件

在局部凸空间中考虑集值优化问题（VP）在强有效解意义下的Kuhn-Tucker最优性条件．在近似锥．次类凸假设下利用择一性定理得到了（VP）取得强有效解的必要条件，利用基泛函的性质给出了（VP）取得强有效解的充分条件，最后给出了一种与（VP）等价的无约束规划。     

集值优化问题超有效解的Lagrange最优性条件

在局部凸空间中考虑约束集值优化问题（VP）在超有效解意义下的Lagrange最优性条件．在近似锥-次类凸假设下，利用择一性定理得到了（VP）取得强有效解的必要条件，利用超有效解集的性质及超有效解的定义给出了（VP）取得超有效解的充分条件，最后给出了一种与（VP）等价的无约束规划．     

非凸集值优化问题E-Benson真有效元的最优性条件

本文首先给出了集合为近似E-次类凸的等价刻画.其次,分别在锥具有紧基和弱紧基的条件下,获得了近似E-次类凸集值优化问题的E-Benson真有效元的Lagrange乘子定理.作为应用,获得了集值优化问题Benson真有效元的Lagrange乘子定理.最后,给出了集值优化问题E-鞍点的充分条件.     

拟凸函数的近似次微分及其在多目标优化问题中的应用

针对拟凸函数提出一类新的近似次微分,研究其性质,并将近似次微分应用到拟凸多目标优化问题近似解的刻画中.首先,对已有的近似次微分进行改进,得到拟凸函数新的近似次微分,并给出其与已有次微分之间的关系及一系列性质.随后,利用新的近似次微分给出拟凸多目标优化问题近似有效解、近似真有效解的最优性条件.     

向量优化问题(C,ε)-弱有效解的一个非线性标量化性质

Gutierrez等在co-radiant集的基础上提出了一种新的(C,ε)-弱有效解,它统一了之前文献中提出的几种经典的近似解.利用由Gpfert等提出的一类非线性标量化函数,给出了(C,ε)-弱有效解的一个等价性质.最后,给出一个例子说明主要结果.     

向量优化中基于拟内部的弱C(ε)-有效解的标量化

首先获得了co-radiant集的一些拟内部性质.进而在邻近C(ε)-次似凸性假设条件下,建立了相应的择一性定理,并给出了基于拟内部的集值向量优化问题弱C(ε)-有效解的线性标量化结果.     

Abstract convexity of radiant functions with applications

In this paper, we investigate abstract convexity of non-positive increasing and radiant (IR) functions over a topological vector space. We characterize the essential results of abstract convexity such as support set, subdifferential and polarity of these functions. We also give some characterizations of a certain kind of polarity and separation property for non-convex radiant and co-radiant sets.     

Monotonic analysis over ordered topological vector spaces: IV

In this paper, we present an extension for non-negative increasing and co-radiant (ICR) functions over a topological vector space. We characterize the essential results of abstract convexity such as support set, subdifferential and polarity of these functions. We also give some characterizations of a certain kind of polarity and separation property for non-convex radiant and co-radiant sets.     

On the convergence of inexact augmented Lagrangian methods for problems with convex constraints

We consider the augmented Lagrangian method (ALM) for constrained optimization problems in the presence of convex inequality and convex abstract constraints. We focus on the case where the Lagrangian sub-problems are solved up to approximate stationary points, with increasing accuracy. We analyze two different criteria of approximate stationarity for the sub-problems and we prove the global convergence to stationary points of ALM in both cases.     

带约束条件集值优化问题近似Henig有效解集的连通性

研究了带约束条件集值优化问题近似Henig有效解集的连通性.在实局部凸Hausdorff空间中,讨论了可行域为弧连通紧的,目标函数为C-弧连通的条件下,带约束条件集值优化问题近似Henig有效解集的存在性和连通性.并给出了带约束条件集值优化问题近似Henig有效解集的连通性定理.     

ε-Pareto Optimality Conditions for Convex Multiobjective Programming via Max Function

We consider a nondifferentiable convex multiobjective optimization problem whose feasible set is defined by affine equality constraints, convex inequality constraints, and an abstract convex set constraint. We obtain Fritz John and Kuhn–Tucker necessary and sufficient conditions for ε-Pareto optimality via a max function. We also provide some relations among ε-Pareto solutions for such a problem and approximate solutions for several associated scalar problems.     

Self-Concordant Barriers for Convex Approximations of Structured Convex Sets

We show how to approximate the feasible region of structured convex optimization problems by a family of convex sets with explicitly given and efficient (if the accuracy of the approximation is moderate) self-concordant barriers. This approach extends the reach of the modern theory of interior-point methods, and lays the foundation for new ways to treat structured convex optimization problems with a very large number of constraints. Moreover, our approach provides a strong connection from the theory of self-concordant barriers to the combinatorial optimization literature on solving packing and covering problems.     

KKT conditions for weak compact convex sets,theorems of the alternative,and optimality conditions

We present several equivalent conditions for the Karush–Kuhn–Tucker conditions for weak^? compact convex sets. Using them, we extend several existing theorems of the alternative in terms of weak^? compact convex sets. Such extensions allow us to express the KKT conditions and hence necessary optimality conditions for more general nonsmooth optimization problems with inequality and equality constraints. Furthermore, several new equivalent optimality conditions for optimization problems with inequality constraints are obtained.     

Hilbertian convex feasibility problem: Convergence of projection methods

The classical problem of finding a point in the intersection of countably many closed and convex sets in a Hilbert space is considered. Extrapolated iterations of convex combinations of approximate projections onto subfamilies of sets are investigated to solve this problem. General hypotheses are made on the regularity of the sets and various strategies are considered to control the order in which the sets are selected. Weak and strong convergence results are established within thisbroad framework, which provides a unified view of projection methods for solving hilbertian convex feasibility problems. This work was supported by the National Science Foundation under Grant MIP-9308609.